1. DEVELOPMENT OF ONLINE EVENT SELECTION IN CBM DEVELOPMENT OF ONLINE EVENT SELECTION IN CBM I. Kisel (for CBM Collaboration) I. Kisel (for CBM Collaboration) GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany; I.Kisel@gsi.de GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany; E-mail: I.Kisel@gsi.de Open Charm Event Selection D (c = 312 m): D + K - + + (9.5%) D 0 (c = 123 m): D 0 K - + (3.8%) D 0 K - + + - (7.5%) D s (c = 150 m): D + s K + K - + (5.3%) + c (c = 60 m): + c pK - + (5.0%) No simple trigger primitive, like high p t, available to tag events of interest. The only selective signature is the detection of the decay vertex. Very efficient tracking algorithms are essential for the feasibility of the open charm event selection Up to 10 9 tracks/sec in the Silicon Tracker Develop algorithms which exploit the full potential of modern processors. First step: -use SIMD instructions Best results were obtained with a Cellular Automaton based track finder with integrated Kalman filter track fit allows usage of double-side strip detectors even at high track densities highly optimized code - field approximated by polynomials - compact, cache-efficient data - most calculations SIMDized - fast on standard PC's - well adapted to next generation many-core and wide-SIMD processors - already ported to IBM Cell processor and NVIDIA graphics cards very fast when only hard quasi-primary tracks are reconstructed, as needed in the online first level event selection of open charm candidates supports reconstruction of soft tracks down to 100 MeV/c, as needed in the offline analysis High Speed Tracking Algorithms Source: CBM Progress Report, 2008. Cell: Heterogeneous multi-core Intel P4 Cell lxg1411 eh102 blade11bc4 Optimization steps for the track fit routine Performance on different platforms CPU time for track reconstruction and fit Typical reconstructed Au+Au collision Concept of SIMD R&D Roadmap Detailed simulation and co-optimization of the tracking system and the analysis algorithms -alternate sensor types (single-sided sensors) -alternate module layouts Detailed studies of event selection algorithms - open charm selector covering all relevant channels (D 0,D ±,D s,Λ c ) -design of multi-level event selection Mathematical and computational optimization of all algorithms Determine best platform for: -Hit/Cluster finding -Tracklet finding -Tracking/Vertexting Go beyond SIMDization (from scalars to vectors) Address MIMDization (multi-threads, multi-cores and many-core systems) Exploit the numerical throughput of dedicated purpose processors like GPU's (Graphics Processors) Be ready for the emerging heterogeneous many-core systems Re-design algorithms to run efficiently on all CPU/GPU architectures Investigate new languages for the performance critical core of algorithms, like OpenCL, Ct or CUDA CPU/GPU AMD: Fusion AMD: FusionCPU/GPU OpenCL?OpenCL? Gaming STI: Cell STI: CellGaming GP CPU Intel: Larrabee Intel: Larrabee GP CPU Intel: Larrabee Intel: Larrabee GP GPU Nvidia: Tesla Nvidia: Tesla GP GPU Nvidia: Tesla Nvidia: Tesla CPU Intel: XXX-cores Intel: XXX-coresCPU FPGA Xilinx XilinxFPGA CPU: SIMD, multi-core GPU: Controller plus many ALU Deutsche Physikalische Gesellschaft e.V. Bochum 09 Tracking Challenge Fixed-target heavy-ion experiment 10 7 Au+Au collisions/sec ~ 1000 charged particles/collision Non-homogeneous magnetic field Double-sided strip detectors Track reconstruction in STS/MVD and displaced vertex search required in the first trigger level Scalability on Intel multi-core CPUs Porting to NVIDIA CUDA Cores HW Threads SIMD width N speed-up = N cores *(N threads /2)*W SIMD K-K-K-K- + First level event selection is done in a processor farm fed with data from the event building network FPGA PCPCPCPCPCSub-Farm Winner of the DPG Poster Session 2009